diff --git a/scripts/README.md b/scripts/README.md
index e8bd401..56bc60e 100644
--- a/scripts/README.md
+++ b/scripts/README.md
@@ -26,7 +26,7 @@
 - Convert your time series dataset into a GluonTS-compatible file dataset. We recommend using the arrow format. You may use the `convert_to_arrow` function from the following snippet for that. Optionally, you may use [synthetic data from KernelSynth](#generating-synthetic-time-series-kernelsynth) to follow along.
     ```py
     from pathlib import Path
-    from typing import List, Optional, Union
+    from typing import List, Union
 
     import numpy as np
     from gluonts.dataset.arrow import ArrowWriter
@@ -35,18 +35,26 @@
     def convert_to_arrow(
         path: Union[str, Path],
         time_series: Union[List[np.ndarray], np.ndarray],
-        start_times: Optional[Union[List[np.datetime64], np.ndarray]] = None,
         compression: str = "lz4",
     ):
-        if start_times is None:
-            # Set an arbitrary start time
-            start_times = [np.datetime64("2000-01-01 00:00", "s")] * len(time_series)
+        """
+        Store a given set of series into Arrow format at the specified path.
 
-        assert len(time_series) == len(start_times)
+        Input data can be either a list of 1D numpy arrays, or a single 2D
+        numpy array of shape (num_series, time_length).
+        """
+        assert isinstance(time_series, list) or (
+            isinstance(time_series, np.ndarray) and
+            time_series.ndim == 2
+        )
+
+        # Set an arbitrary start time
+        start = np.datetime64("2000-01-01 00:00", "s")
 
         dataset = [
-            {"start": start, "target": ts} for ts, start in zip(time_series, start_times)
+            {"start": start, "target": ts} for ts in time_series
         ]
+
         ArrowWriter(compression=compression).write_to_file(
             dataset,
             path=path,
@@ -59,7 +67,6 @@
 
         # Convert to GluonTS arrow format
         convert_to_arrow("./noise-data.arrow", time_series=time_series)
-
     ```
 - Modify the [training configs](training/configs) to use your data. Let's use the KernelSynth data as an example.
     ```yaml